In [236]:
# Baseline predictions
baseline_preds = model3.predict([X_num_test, X_cat_test]).flatten()
baseline_mse = mean_squared_error(y_test, baseline_preds)
feature_names = [
'unemployment_rate', 'exchange_rate', 'cpi_lag_1',
'overnights_lag_1', 'overnights_lag_3', 'overnights_lag_6', 'overnights_lag_12',
'google_trends_lag_1', 'google_trends_lag_3', 'schengen_member', 'euro_adopted']+month_cols
importances = []
for i in range(X_num_test.shape[1]):
X_permuted = X_num_test.copy()
X_permuted[:, i] = np.random.permutation(X_permuted[:, i])
permuted_preds = model3.predict([X_permuted, X_cat_test]).flatten()
permuted_mse = mean_squared_error(y_test, permuted_preds)
importances.append(max(0, permuted_mse - baseline_mse))
#Handle grouped month dummies so its not one bar for each month
month_indices = [i for i, f in enumerate(feature_names) if f.startswith('month_')]
if month_indices:
# Safe grouped permutation
X_month_permuted = X_num_test.copy()
row_perm = np.random.permutation(len(X_month_permuted))
X_month_permuted[:, month_indices] = X_month_permuted[row_perm][:, month_indices]
month_preds = model3.predict([X_month_permuted, X_cat_test]).flatten()
month_importance = max(0, mean_squared_error(y_test, month_preds) - baseline_mse)
else:
month_importance = 0 # fallback if no month cols present
filtered_names = [f for i, f in enumerate(feature_names) if i not in month_indices]
filtered_importances = [imp for i, imp in enumerate(importances) if i not in month_indices]
feature_names_final = filtered_names + ['month_group']
importances_final = filtered_importances + [month_importance]
sorted_pairs = sorted(zip(importances_final, feature_names_final), reverse=True)
importances_sorted, feature_names_sorted = zip(*sorted_pairs)
plt.figure(figsize=(12, 6))
bars = plt.barh(feature_names_sorted, importances_sorted)
plt.xlabel("Increase in MSE when shuffled")
plt.title("Model 3: Overnights (2024)")
plt.gca().invert_yaxis()
# Add labels
for bar in bars:
width = bar.get_width()
plt.text(width + 0.001, bar.get_y() + bar.get_height() / 2,
f"{width:.4f}", va='center')
plt.tight_layout()
plt.show()
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